[论文解读] Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

Yang Li, Kang Li+4 more|arXiv (Cornell University)|Nov 24, 2021

Microgrid Control and Optimization参考文献 49被引用 158

一句话总结

本文提出了一种集成可再生能源和需求响应的孤岛微电网双层随机最优调度模型，采用一种新颖的Jaya-IPM混合算法，结合内点法（IPM）求解上层微电网成本最小化问题与Jaya算法求解下层用户成本优化问题。与HIA相比，该方法使微电网净收益提高10.9%，用户成本降低6.1%，计算速度提升90%，有效平衡了供需关系，并通过协调负荷灵活性与可再生能源不确定性管理实现了峰值负荷削减。

ABSTRACT

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower- levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency.

研究动机与目标

• 为解决在实时电价机制下，孤岛微电网（IMGs）中不确定可再生能源出力与需求响应之间的协调挑战。
• 通过双层优化框架平衡微电网运行成本最小化与用户成本降低之间的冲突目标。
• 为由此产生的双层随机规划问题开发一种计算高效且精确的求解方法。
• 展示需求响应在实现峰值负荷削减与提升系统灵活性方面的有效性。

提出的方法

• 构建双层规划模型：上层最小化微电网运行成本，下层在实时电价下最小化用户成本。
• 提出Jaya-IPM混合算法，其中IPM求解上层混合整数线性规划（MILP）问题，Jaya算法优化下层问题。
• 通过反馈回路迭代更新上层的实时电价与下层的电能计划，实现联合优化。
• 采用机会约束规划（CCP）处理可再生能源出力与负荷的不确定性，设定在预设置信水平γ下的概率约束。
• 模型整合了储能系统（ESS）、可转移负荷与旋转备用，以确保供需平衡与系统可靠性。
• 采用包含光伏（PV）、风力发电机（WT）、微燃气轮机（MT）与Zn-Br电池的园区级微电网案例进行验证。

实验结果

研究问题

• RQ1 如何在孤岛微电网调度中有效协调需求响应与可再生能源出力的不确定性，以平衡微电网与用户利益？
• RQ2 在集成需求响应后，实时电价对负荷转移与系统成本有何影响？
• RQ3 所提出的Jaya-IPM算法在性能与效率上相较于传统求解器（CPLEX）与混合智能算法（HIA）表现如何？
• RQ4 在高比例可再生能源渗透的微电网中，需求响应在多大程度上可降低峰值负荷与旋转备用需求？

主要发现

• 与HIA相比，所提出的Jaya-IPM方法在可转移负荷比例为20%时，使微电网净收益提高10.9%，与CPLEX相比提高11.9%。
• 与HIA相比，用户成本降低6.1%，与CPLEX相比降低7.7%，表明用户成本效率更优。
• 与HIA相比，计算时间减少约90%；与CPLEX相比，减少60%，表明计算效率显著更优。
• 该模型成功通过将可转移负荷从高峰时段转移至低谷时段，实现峰值负荷削减，降低了高峰时段对旋转备用的需求。
• 储能系统（ESS）的充放电循环次数在所提方法下显著增加，表明ESS被有效用于平衡可再生能源波动。
• 在需求响应作用下，等效负荷曲线更加平滑，证实其在提升系统灵活性与降低波动性方面的有效性。